Papers › Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition
Duc-Anh Nguyen, Cuong Pham, Nhien-An Le-Khac
Various types of sensors can be used for Human Activity Recognition (HAR), and each of them has different strengths and weaknesses. Sometimes a single sensor cannot fully observe the user's motions from its perspective, which causes wrong predictions. While sensor fusion provides more information for HAR, it comes with many inherent drawbacks like user privacy and acceptance, costly set-up, operation, and maintenance. To deal with this problem, we propose Virtual Fusion - a new method that takes advantage of unlabeled data from multiple time-synchronized sensors during training, but only needs one sensor for inference. Contrastive learning is adopted to exploit the correlation among sensors. Virtual Fusion gives significantly better accuracy than training with the same single sensor, and in some cases, it even surpasses actual fusion using multiple sensors at test time. We also extend this method to a more general version called Actual Fusion within Virtual Fusion (AFVF), which uses a subset of training sensors during inference. Our method achieves state-of-the-art accuracy and F1-score on UCI-HAR and PAMAP2 benchmark datasets. Implementation is available upon request.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human Activity Recognition | HAR | AFVF | Accuracy | 0.9861 | #2 of 2 | Archive leaderboard | report |
| Human Activity Recognition | HAR | AFVF | Macro-F1 | 0.9865 | #2 of 2 | Archive leaderboard | report |
| Human Activity Recognition | PAMAP2 | AFVF | Accuracy | 0.9672 | #3 of 3 | Archive leaderboard | report |
| Human Activity Recognition | PAMAP2 | AFVF | Macro F1 | 0.9665 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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